Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo

Bayesian and Monte Carlo change-point detection

The contribution presents to analyses and comparison of the recursive (sliding window) Bayesian autoregressive normalized change-point detector (RBACDN) and the reversible jump Markov chain Monte Carlo method (RJMCMC) when they are used for the localization of signal changes (change-point detection). The choice of priors and parameter setting for the RJMCMC and the RBACDN are discussed. The eva...

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Annealing Stochastic Approximation Monte Carlo for Global Optimization

During the past several decades, simulated annealing (Kirkpatrick et al., 1983) and the genetic algorithm (Holland, 1975; Goldberg, 1989) have been applied successfully by many authors to highly complex optimization problems in different fields of sciences and engineering. In spite of their successes, both algorithms suffer from some difficulties in convergence to the global optima. Suppose tha...

Bayesian phylogeny analysis via stochastic approximation Monte Carlo.

Monte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algo...

Particle Markov Chain Monte Carlo for Multiple Change-point Problems

Multiple change-point models are a popular class of time series models which allow the description of temporal heterogeneity in data. We develop efficient Markov Chain Monte Carlo (MCMC) algorithms to perform Bayesian inference in this context. Our so-called Particle MCMC (PMCMC) algorithms rely on an efficient Sequential Monte Carlo (SMC) technique for change-point models, developed in [13], t...